Computer vision techniques are increasingly used to automatically detect or classify objects or events in images. For example, behavior modeling techniques are often applied to identify human behavior patterns. Typically, behavior analysis and modeling systems initially learn the patterns of behavior and thereafter detect deviations from such patterns of behavior. Generally, conventional behavior analysis techniques are focused on the detection of unusual events.
Most prior work in behavior modeling has been concerned with the modeling of trajectories either as probability distributions or Hidden Markov Models (HMMs). Once the trajectories are modeled, the goal is to predict the trajectory of the objects and to detect “unusual” trajectories. For example, trajectory analysis techniques have been applied to observe the trajectories of persons walking in a particular area and to thereafter generate an alarm if a trajectory indicates that a person has entered a restricted area.
While conventional trajectory analysis techniques perform well for many security applications, the validity and value of such trajectory analysis techniques for other applications, such as home monitoring, is questionable. In particular, when a behavior modeling system is monitoring the activities of one or more particular people, such as the remote monitoring of an elderly person in a home environment, the most relevant information is the current location or activity of the person, as opposed to how the person arrived at his or her current position or activity.
A need therefore exists for behavior modeling and detection methods and systems that identify an event by observing patterns of behavior and detecting a violation of a repetitive pattern of behavior.